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VEHICLE LIVE RISK PREDICTION #529
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Our team will soon review your PR. Thanks @CoderOMaster :) |
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- Put all the EDA results in the Images folder.
- Can you implement Lasso, Ridge, XgBoost, Gradient Boosting and MLP in this dataset? That'll be a better comparison and model implementation showcase.
Hi @abhisheks008 |
I know it's not achieve good scores always. If you implement all the mentioned models, others can benefit from this while going through your project, if someone wants to know more about the model implementation on a same dataset. |
@abhisheks008 I have implemented all and xgboost in another notebook separately due to some detection issue in garuda linux.All images are added too for eda.I would like to request you that please consider this as hard problem since it took lot of analysis to conclude. |
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Requested some changes related to documentation.
@CoderOMaster
This will be marked as Hard label, don't worry, your efforts will get the value as well.
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These sample images show relationship of columns with each other(more detail in notebook 1 with EDA) | ||
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**MODELS USED** |
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Update the model names as per the recent upgrade.
4) MATPLOTLIB | ||
5) SEABORN | ||
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**IMPLEMENTATION** |
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Update this too.
4) 50+ risk_score was considered as parameter for accident risk,whereas less than value signified no risk of accident. | ||
4) Different notebooks with different models were used for clear and concise display of information for mentor. | ||
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**RESULT** |
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Update the results section too. It's better to create a table like this,
Models implemented | Accuracy scores |
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Random Forest | 99% |
... | ... |
... | ... |
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done sir
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Approved.
@CoderOMaster
Please share your email ID for further communication. |
@abhisheks008 |
Pull Request for ML-Crate 💡
Issue Title: VEHICLE LIVE RISK PREDICTION
Closes: #issue number 473 that will be closed through this PR